psygnisfive

Joined 19 years ago

Karma

436

Submissions

126

Member since

Sep 1

Beka Valentine; trans dyke and general hacker bitch.

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Recent submissions

Comment · 3 years ago

ThePirateBay is not sticking ads in BitTorrented Video shows and movies no matter how little you pay

Comment · 4 years ago

Contact the Electronic Frontier Foundation. This is literally what they do. I don't know if they'll take up the case, but talk to them.

Comment · 7 years ago

> Security vulnerability finding is almost certainly the wrong target for Semmle

CVE-2019-5876

CVE-2019-16230

CVE-2019-16231

CVE-2019-16232

CVE-2019-16233

CVE-2019-16234

CVE-2019-15026

CVE-2019-14192

CVE-2019-14193

CVE-2019-14194

CVE-2019-14195

CVE-2019-14196

CVE-2019-14197

CVE-2019-14198

CVE-2019-14199

CVE-2019-14200

CVE-2019-14201

CVE-2019-14202

CVE-2019-14203

CVE-2019-14204

CVE-2019-14437

CVE-2019-14438

CVE-2019-14498

CVE-2019-14535

CVE-2019-14534

CVE-2019-14533

CVE-2019-14776

CVE-2019-14778

CVE-2019-14779

CVE-2019-14777

CVE-2019-14970

CVE-2019-15119

CVE-2019-14524

CVE-2019-14523

CVE-2019-7307

CVE-2019-11476

CVE-2019-13115

CVE-2019-3570

CVE-2019-13110

CVE-2019-13112

CVE-2019-13113

CVE-2019-13108

CVE-2019-13109

CVE-2019-13111

CVE-2019-13114

CVE-2019-3560

CVE-2019-9721

CVE-2019-9718

CVE-2019-9717

CVE-2019-9720

CVE-2019-9719

CVE-2018-20222

CVE-2019-3828

CVE-2019-6986

CVE-2019-5414

CVE-2018-4460

CVE-2018-16491

CVE-2018-16489

CVE-2018-16490

CVE-2018-19476

CVE-2018-19477

CVE-2018-19475

CVE-2018-19134

CVE-2018-16472

CVE-2018-18820

CVE-2018-4407

CVE-2018-16487

CVE-2018-4259

CVE-2018-4286

CVE-2018-4287

CVE-2018-4288

CVE-2018-4291

CVE-2019-5413

CVE-2018-16461

CVE-2018-16469

CVE-2018-16486

CVE-2018-16460

CVE-2018-16492

CVE-2018-11776

CVE-2018-8018

CVE-2018-8294

CVE-2018-4249

CVE-2018-8013

CVE-2018-5388

CVE-2018-1295

CVE-2018-4136

CVE-2018-4160

CVE-2018-1000140

CVE-2017-15692

CVE-2017-15693

CVE-2017-13904

CVE-2017-15089

CVE-2018-6834

CVE-2018-6835

CVE-2017-15713

CVE-2017-12634

CVE-2017-13782

CVE-2017-7545

CVE-2017-14949

CVE-2017-14868

CVE-2017-8046

CVE-2017-8045

CVE-2017-9805

CVE-2017-1000207

CVE-2017-1000208

CVE-2017-12612

CVE-2017-0141

https://lgtm.com/security/

Comment · 9 years ago

mm.. perhaps I'm just not familiar enough with prolog, but I don't think it's possible to extract out a set of Prolog-level metavariables and compute over them? This is what's necesary to turn something like `_G947 -> _G947` into `forall a. a -> a`. but probably I just don't know enough. cool if you can, tho!

Comment · 9 years ago

I'd also recommend Practical Foundations for Programming Languages (PFPL). And a bunch of other things, which I outlined here:

So You Want To Learn Type Theory (http://purelytheoretical.com/sywtltt.html)

Comment · 9 years ago

Indeed! Tho this is only half the problem, because `T` there includes metavariables, so it's not a type but rather a schema at the meta-level for types. What we'd like is something like `forall a b c. (b -> c) -> (a -> b) -> a -> c` so that composition can be used at arbitrary choices of the types. So like, while we can use this composition function at any particular place we need composition, we can't define `compose` to have this type because the first use site will force the metavars to have a particular value and then we get a nasty global monomorphism. We need to explicitly have metavariables in the language itself, rather than at the metalevel of doing the synthesis/checking.

Comment · 9 years ago

Ahh, well! That's a trickier problem! You need a bidirectional system that has unification for metavariables, and then some system for handling unsolved metavars. But to do that requires a more complicated set up. The goal of the post was to introduce the core ideas and show that it's relatively simple to do decent chunks of this stuff. :)

Comment · 9 years ago

It was also front page on HN at the beginning of 2015 ;)

Comment · 9 years ago

Yeah, we're cross posting from my blog to the official IOHK blog, too. The first post only just got up on IOHK :p

Comment · 9 years ago

Oh! Was Murray Shanahan's lab involved with this? Cool. I've chatted with Murray a very tiny bit on twitter about this all. That paper is part of what inspired me to write this blog post, because I was dissatisfied with the approach they propose.

It felt to me like they had found a way to restrict "symbolic" data to a very narrow domain where issues like hierarchical structure was totally absent, and vectorial representation was therefore quite straight forward, and I wanted to figure out an alternative. Instead of restricting the kinds of symbolic data involved, do the full spectrum. I had the symbolic vector idea in my head for about two years as an answer to the representation problem, but I hadn't seen what the right answer was to the other aspects, at least not clearly. But that paper motivated me to start thinking about it again, and this time I saw the answer. :)

Comment · 9 years ago

That's correct, yes. Cycles don't affect subgraph counts. There's still only finitely many.

Comment · 9 years ago

Reading a graph back can't be done with any certainty, nor is it intended to be done. It's a representation that's intended to be used to build inputs to ML systems, not outputs. Building symbolic outputs should be done via the other techniques mentioned in the blog post (eg. structural editing), that way you can take advantage of notions of correctness inherent in the symbolic representation.

Regarding handling lossiness, it's not clear to what extent it needs to be handled, but the `k` mentioned in the definition of symbolic vectors is a parameter, and the larger `k` is, the less lossy the representation.

Comment · 9 years ago

Oh! Thanks for pointing this out. The whole site is hand maintained, and I routinely forget to put stuff up there. :)

Comment · 9 years ago

I think to some extent it disappeared, to some extent it rebranded. But mostly it disappeared, because commercial motivations pushed a lot of money towards machine learning research. But that's just a hunch.

Comment · 9 years ago

Grammar inference is a very interesting (tho quite different) topic. We have absolutely no idea how humans do it, and we know that its really really hard (nearly impossible!) to do given just raw text. Super interesting topic!